Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Лонгитюдное корреляционное исследование× | Лонгитюдное исследование× | |
|---|---|---|
| Область | Дизайн исследования | Дизайн исследования |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | Mid-20th century (formalized 1940s–1960s) | Late 19th–early 20th century; methodologically codified through the 20th century |
| Автор метода≠ | Rooted in early correlational methodology (Galton, Pearson late 19th c.); longitudinal extension formalized through panel studies in social sciences (mid-20th c.) | No single originator; foundational methodological treatments by Stuart Menard and Judith Singer & John Willett |
| Тип≠ | Non-experimental quantitative design | Quantitative (or mixed) observational research design |
| Основополагающий источник≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2009). How to Design and Evaluate Research in Education (8th ed.). McGraw-Hill. ISBN: 978-0078097898 | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922841 |
| Другие названия | longitudinal correlational study, prospective correlational design, longitudinal associational research, repeated-measures correlational design | longitudinal study, longitudinal design, prospective longitudinal study, repeated-measures observational study |
| Связанные≠ | 3 | 4 |
| Сводка≠ | Longitudinal correlational research is a non-experimental quantitative design that examines the strength and direction of relationships among variables by collecting data from the same participants at two or more points in time. Unlike a cross-sectional correlational study, the longitudinal approach captures how associations evolve, persist, or dissolve across time, providing a stronger empirical basis for causal inference without experimental manipulation. | Longitudinal research is an observational design in which the same participants, groups, or units are measured repeatedly over an extended period. Rather than capturing a single snapshot, it tracks change, stability, and temporal sequencing of variables — making it the primary non-experimental strategy for studying development, growth, decline, and the unfolding of causal processes across time. |
| ScholarGateНабор данных ↗ |
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